{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "718464bc-d3bc-4231-ae00-2a3bff2fab68",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import xgboost as xgb\n",
    "import seaborn as sns \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy as np\n",
    "np.int = int\n",
    "import matplotlib.pyplot as plt\n",
    "#导入各种用于评估模型性能和参数优化的函数和类，例如均方误差（MSE）、均绝对误差（MAE）、决定系数（R2 Score）、解释方差分（explained variance score）等\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score,explained_variance_score,mean_absolute_percentage_error,mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "#导入XGBoost库中的XGBClassifier类，用于建立XGBoost分类模型，并导入plot_importance函数用于特征重要性可视化。\n",
    "from xgboost import XGBClassifier, plot_importance\n",
    "#导入互信息函数\n",
    "from sklearn.feature_selection import mutual_info_regression\n",
    "#导入lasso模型筛选特征变量\n",
    "from sklearn.linear_model import Lasso\n",
    "#导入Scikit-Optimize库中的贝叶斯优化相关的函数和类，用于超参数调优\n",
    "from skopt import gp_minimize\n",
    "from skopt.space import Real, Integer\n",
    "#导入交叉验证函数，用于评估模型性能\n",
    "from sklearn.model_selection import cross_val_score\n",
    "#导入随机森林回归模型\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "plt.rcParams['font.family'] = 'Times New Roman'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "50045870-09f1-4bd7-b236-4bafb6c90da6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#df = pd.read_excel('指标.xlsx')\n",
    "d = pd.read_excel(r'D:\\work\\暑期工作\\0606_重新处理\\河南省小麦产量预测\\数据\\Feature.xlsx')\n",
    "dd = pd.read_excel(r'D:\\work\\暑期工作\\0606_重新处理\\河南省小麦产量预测\\数据\\Features.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fc42cdc5-0483-4439-98af-3593081eff03",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "d=d.drop(columns=['Unnamed: 0'])\n",
    "dd=dd.drop(columns=['Unnamed: 0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "59285029-518e-41c4-8d69-a0f2c2da0cba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>AvgSurfT_inst_chengshu</th>\n",
       "      <th>LST_Day_1km_chousui</th>\n",
       "      <th>LST_Night_1km_fennie</th>\n",
       "      <th>soil_temperature_level_2_kaihua</th>\n",
       "      <th>soil_temperature_level_2_guanjiang</th>\n",
       "      <th>soil_temperature_level_2_shouhuo</th>\n",
       "      <th>soil_temperature_level_3_chousui</th>\n",
       "      <th>soil_temperature_level_3_kaihua</th>\n",
       "      <th>soil_temperature_level_3_guanjiang</th>\n",
       "      <th>...</th>\n",
       "      <th>SVAI_chousui</th>\n",
       "      <th>SVAI_kaihua</th>\n",
       "      <th>WDRVI_bajie</th>\n",
       "      <th>WDRVI_chousui</th>\n",
       "      <th>WDVI_bajie</th>\n",
       "      <th>WDVI_yunsui</th>\n",
       "      <th>WDVI_chousui</th>\n",
       "      <th>WDVI_kaihua</th>\n",
       "      <th>wet_kaihua</th>\n",
       "      <th>亩产</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>410522</td>\n",
       "      <td>301.830072</td>\n",
       "      <td>14770.448120</td>\n",
       "      <td>13653.260039</td>\n",
       "      <td>291.679432</td>\n",
       "      <td>294.485981</td>\n",
       "      <td>301.239969</td>\n",
       "      <td>287.263466</td>\n",
       "      <td>288.780851</td>\n",
       "      <td>290.517675</td>\n",
       "      <td>...</td>\n",
       "      <td>0.509428</td>\n",
       "      <td>0.502203</td>\n",
       "      <td>-0.270294</td>\n",
       "      <td>0.038094</td>\n",
       "      <td>0.312649</td>\n",
       "      <td>0.346212</td>\n",
       "      <td>0.380784</td>\n",
       "      <td>0.355850</td>\n",
       "      <td>-0.090609</td>\n",
       "      <td>7467.000138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>410421</td>\n",
       "      <td>301.881276</td>\n",
       "      <td>14811.998245</td>\n",
       "      <td>13724.830174</td>\n",
       "      <td>291.624652</td>\n",
       "      <td>294.462918</td>\n",
       "      <td>301.229017</td>\n",
       "      <td>287.616328</td>\n",
       "      <td>289.032764</td>\n",
       "      <td>290.784441</td>\n",
       "      <td>...</td>\n",
       "      <td>0.456349</td>\n",
       "      <td>0.444113</td>\n",
       "      <td>-0.220879</td>\n",
       "      <td>0.020097</td>\n",
       "      <td>0.272293</td>\n",
       "      <td>0.321097</td>\n",
       "      <td>0.313054</td>\n",
       "      <td>0.308834</td>\n",
       "      <td>-0.088144</td>\n",
       "      <td>5972.666001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>411726</td>\n",
       "      <td>298.990822</td>\n",
       "      <td>15035.873561</td>\n",
       "      <td>13760.252546</td>\n",
       "      <td>291.141219</td>\n",
       "      <td>293.187955</td>\n",
       "      <td>298.777454</td>\n",
       "      <td>287.204259</td>\n",
       "      <td>288.763693</td>\n",
       "      <td>290.271875</td>\n",
       "      <td>...</td>\n",
       "      <td>0.375751</td>\n",
       "      <td>0.406768</td>\n",
       "      <td>-0.273682</td>\n",
       "      <td>-0.176015</td>\n",
       "      <td>0.255247</td>\n",
       "      <td>0.291260</td>\n",
       "      <td>0.276982</td>\n",
       "      <td>0.277341</td>\n",
       "      <td>-0.107184</td>\n",
       "      <td>5687.791498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>410822</td>\n",
       "      <td>302.326105</td>\n",
       "      <td>14909.855104</td>\n",
       "      <td>13684.031442</td>\n",
       "      <td>291.393057</td>\n",
       "      <td>294.128413</td>\n",
       "      <td>300.920627</td>\n",
       "      <td>286.732262</td>\n",
       "      <td>288.209019</td>\n",
       "      <td>290.136585</td>\n",
       "      <td>...</td>\n",
       "      <td>0.466787</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>-0.305916</td>\n",
       "      <td>0.009030</td>\n",
       "      <td>0.277992</td>\n",
       "      <td>0.313744</td>\n",
       "      <td>0.335973</td>\n",
       "      <td>0.327689</td>\n",
       "      <td>-0.062063</td>\n",
       "      <td>7959.597222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>411082</td>\n",
       "      <td>302.665974</td>\n",
       "      <td>14764.439462</td>\n",
       "      <td>13685.820730</td>\n",
       "      <td>292.812644</td>\n",
       "      <td>295.863324</td>\n",
       "      <td>302.456445</td>\n",
       "      <td>288.378758</td>\n",
       "      <td>289.948855</td>\n",
       "      <td>291.813767</td>\n",
       "      <td>...</td>\n",
       "      <td>0.479437</td>\n",
       "      <td>0.456340</td>\n",
       "      <td>-0.155182</td>\n",
       "      <td>0.027083</td>\n",
       "      <td>0.329961</td>\n",
       "      <td>0.356682</td>\n",
       "      <td>0.342203</td>\n",
       "      <td>0.353976</td>\n",
       "      <td>-0.074036</td>\n",
       "      <td>7662.496889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>411081</td>\n",
       "      <td>302.379726</td>\n",
       "      <td>14864.959928</td>\n",
       "      <td>13716.550674</td>\n",
       "      <td>292.036420</td>\n",
       "      <td>294.927847</td>\n",
       "      <td>301.419777</td>\n",
       "      <td>287.740673</td>\n",
       "      <td>289.256381</td>\n",
       "      <td>291.034992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.438036</td>\n",
       "      <td>0.427608</td>\n",
       "      <td>-0.222630</td>\n",
       "      <td>-0.033628</td>\n",
       "      <td>0.276777</td>\n",
       "      <td>0.307460</td>\n",
       "      <td>0.306785</td>\n",
       "      <td>0.308283</td>\n",
       "      <td>-0.090942</td>\n",
       "      <td>6613.422500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>411424</td>\n",
       "      <td>302.404313</td>\n",
       "      <td>14767.178119</td>\n",
       "      <td>13698.554350</td>\n",
       "      <td>293.142870</td>\n",
       "      <td>296.125191</td>\n",
       "      <td>302.061064</td>\n",
       "      <td>288.739608</td>\n",
       "      <td>290.273988</td>\n",
       "      <td>292.107970</td>\n",
       "      <td>...</td>\n",
       "      <td>0.537977</td>\n",
       "      <td>0.518776</td>\n",
       "      <td>-0.049587</td>\n",
       "      <td>0.143995</td>\n",
       "      <td>0.356591</td>\n",
       "      <td>0.372768</td>\n",
       "      <td>0.380640</td>\n",
       "      <td>0.392420</td>\n",
       "      <td>-0.074186</td>\n",
       "      <td>7579.469809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>411724</td>\n",
       "      <td>300.297286</td>\n",
       "      <td>14800.494981</td>\n",
       "      <td>13753.180467</td>\n",
       "      <td>291.750152</td>\n",
       "      <td>293.718662</td>\n",
       "      <td>299.219180</td>\n",
       "      <td>287.779530</td>\n",
       "      <td>289.413163</td>\n",
       "      <td>290.932713</td>\n",
       "      <td>...</td>\n",
       "      <td>0.511967</td>\n",
       "      <td>0.563379</td>\n",
       "      <td>0.082507</td>\n",
       "      <td>0.149694</td>\n",
       "      <td>0.389395</td>\n",
       "      <td>0.347135</td>\n",
       "      <td>0.345173</td>\n",
       "      <td>0.381115</td>\n",
       "      <td>-0.079914</td>\n",
       "      <td>5754.140236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>411324</td>\n",
       "      <td>300.250438</td>\n",
       "      <td>14870.302144</td>\n",
       "      <td>13755.946153</td>\n",
       "      <td>291.086794</td>\n",
       "      <td>293.369445</td>\n",
       "      <td>300.014684</td>\n",
       "      <td>287.141116</td>\n",
       "      <td>288.584741</td>\n",
       "      <td>290.356689</td>\n",
       "      <td>...</td>\n",
       "      <td>0.480808</td>\n",
       "      <td>0.460888</td>\n",
       "      <td>-0.102704</td>\n",
       "      <td>0.005744</td>\n",
       "      <td>0.309025</td>\n",
       "      <td>0.317261</td>\n",
       "      <td>0.348503</td>\n",
       "      <td>0.307512</td>\n",
       "      <td>-0.097445</td>\n",
       "      <td>5467.128359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>410122</td>\n",
       "      <td>302.101448</td>\n",
       "      <td>14900.316284</td>\n",
       "      <td>13728.293344</td>\n",
       "      <td>292.494994</td>\n",
       "      <td>295.405081</td>\n",
       "      <td>301.660649</td>\n",
       "      <td>288.002027</td>\n",
       "      <td>289.590761</td>\n",
       "      <td>291.427115</td>\n",
       "      <td>...</td>\n",
       "      <td>0.380461</td>\n",
       "      <td>0.390789</td>\n",
       "      <td>-0.356993</td>\n",
       "      <td>-0.172850</td>\n",
       "      <td>0.260427</td>\n",
       "      <td>0.290878</td>\n",
       "      <td>0.283795</td>\n",
       "      <td>0.284526</td>\n",
       "      <td>-0.095178</td>\n",
       "      <td>6637.200003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       NAME  AvgSurfT_inst_chengshu  LST_Day_1km_chousui   \n",
       "0    410522              301.830072         14770.448120  \\\n",
       "1    410421              301.881276         14811.998245   \n",
       "2    411726              298.990822         15035.873561   \n",
       "3    410822              302.326105         14909.855104   \n",
       "4    411082              302.665974         14764.439462   \n",
       "..      ...                     ...                  ...   \n",
       "97   411081              302.379726         14864.959928   \n",
       "98   411424              302.404313         14767.178119   \n",
       "99   411724              300.297286         14800.494981   \n",
       "100  411324              300.250438         14870.302144   \n",
       "101  410122              302.101448         14900.316284   \n",
       "\n",
       "     LST_Night_1km_fennie  soil_temperature_level_2_kaihua   \n",
       "0            13653.260039                       291.679432  \\\n",
       "1            13724.830174                       291.624652   \n",
       "2            13760.252546                       291.141219   \n",
       "3            13684.031442                       291.393057   \n",
       "4            13685.820730                       292.812644   \n",
       "..                    ...                              ...   \n",
       "97           13716.550674                       292.036420   \n",
       "98           13698.554350                       293.142870   \n",
       "99           13753.180467                       291.750152   \n",
       "100          13755.946153                       291.086794   \n",
       "101          13728.293344                       292.494994   \n",
       "\n",
       "     soil_temperature_level_2_guanjiang  soil_temperature_level_2_shouhuo   \n",
       "0                            294.485981                        301.239969  \\\n",
       "1                            294.462918                        301.229017   \n",
       "2                            293.187955                        298.777454   \n",
       "3                            294.128413                        300.920627   \n",
       "4                            295.863324                        302.456445   \n",
       "..                                  ...                               ...   \n",
       "97                           294.927847                        301.419777   \n",
       "98                           296.125191                        302.061064   \n",
       "99                           293.718662                        299.219180   \n",
       "100                          293.369445                        300.014684   \n",
       "101                          295.405081                        301.660649   \n",
       "\n",
       "     soil_temperature_level_3_chousui  soil_temperature_level_3_kaihua   \n",
       "0                          287.263466                       288.780851  \\\n",
       "1                          287.616328                       289.032764   \n",
       "2                          287.204259                       288.763693   \n",
       "3                          286.732262                       288.209019   \n",
       "4                          288.378758                       289.948855   \n",
       "..                                ...                              ...   \n",
       "97                         287.740673                       289.256381   \n",
       "98                         288.739608                       290.273988   \n",
       "99                         287.779530                       289.413163   \n",
       "100                        287.141116                       288.584741   \n",
       "101                        288.002027                       289.590761   \n",
       "\n",
       "     soil_temperature_level_3_guanjiang  ...  SVAI_chousui  SVAI_kaihua   \n",
       "0                            290.517675  ...      0.509428     0.502203  \\\n",
       "1                            290.784441  ...      0.456349     0.444113   \n",
       "2                            290.271875  ...      0.375751     0.406768   \n",
       "3                            290.136585  ...      0.466787     0.392344   \n",
       "4                            291.813767  ...      0.479437     0.456340   \n",
       "..                                  ...  ...           ...          ...   \n",
       "97                           291.034992  ...      0.438036     0.427608   \n",
       "98                           292.107970  ...      0.537977     0.518776   \n",
       "99                           290.932713  ...      0.511967     0.563379   \n",
       "100                          290.356689  ...      0.480808     0.460888   \n",
       "101                          291.427115  ...      0.380461     0.390789   \n",
       "\n",
       "     WDRVI_bajie  WDRVI_chousui  WDVI_bajie  WDVI_yunsui  WDVI_chousui   \n",
       "0      -0.270294       0.038094    0.312649     0.346212      0.380784  \\\n",
       "1      -0.220879       0.020097    0.272293     0.321097      0.313054   \n",
       "2      -0.273682      -0.176015    0.255247     0.291260      0.276982   \n",
       "3      -0.305916       0.009030    0.277992     0.313744      0.335973   \n",
       "4      -0.155182       0.027083    0.329961     0.356682      0.342203   \n",
       "..           ...            ...         ...          ...           ...   \n",
       "97     -0.222630      -0.033628    0.276777     0.307460      0.306785   \n",
       "98     -0.049587       0.143995    0.356591     0.372768      0.380640   \n",
       "99      0.082507       0.149694    0.389395     0.347135      0.345173   \n",
       "100    -0.102704       0.005744    0.309025     0.317261      0.348503   \n",
       "101    -0.356993      -0.172850    0.260427     0.290878      0.283795   \n",
       "\n",
       "     WDVI_kaihua  wet_kaihua           亩产  \n",
       "0       0.355850   -0.090609  7467.000138  \n",
       "1       0.308834   -0.088144  5972.666001  \n",
       "2       0.277341   -0.107184  5687.791498  \n",
       "3       0.327689   -0.062063  7959.597222  \n",
       "4       0.353976   -0.074036  7662.496889  \n",
       "..           ...         ...          ...  \n",
       "97      0.308283   -0.090942  6613.422500  \n",
       "98      0.392420   -0.074186  7579.469809  \n",
       "99      0.381115   -0.079914  5754.140236  \n",
       "100     0.307512   -0.097445  5467.128359  \n",
       "101     0.284526   -0.095178  6637.200003  \n",
       "\n",
       "[102 rows x 66 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75eb75d7-b0ce-4b35-98b5-91a2ab2470aa",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "### 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bb20839c-6340-44b1-a9e7-3c0c367273c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除特征的个数： 0\n",
      "出错特征的个数： 0\n",
      "删除的特征： []\n"
     ]
    }
   ],
   "source": [
    "#############删除缺失率高于05的特征\n",
    "null_list = []\n",
    "error_list = []\n",
    "for col in d.columns:\n",
    "    try:\n",
    "        null_list.append([col,d[col].isnull().sum() / d.shape[0]])\n",
    "    except:\n",
    "        error_list.append(col)\n",
    "null_df = pd.DataFrame(null_list,columns = ['特征名称','缺失率'])\n",
    "del_cols = null_df['特征名称'][null_df['缺失率'] >= 0.5]\n",
    "print('删除特征的个数：',len(del_cols))\n",
    "print('出错特征的个数：',len(error_list))\n",
    "print('删除的特征：',del_cols.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aba9ae28-97f8-4fec-bc66-dd5fbfd318ec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除特征的个数： 0\n",
      "出错特征的个数： 0\n",
      "删除的特征： []\n"
     ]
    }
   ],
   "source": [
    "#############表格中用绿色标记表示删除同值比例高的特征\n",
    "tz_list = []\n",
    "error_list = []\n",
    "for col in d.columns:\n",
    "    try:\n",
    "        tz_list.append([col,d[d[col] == d[col].mode()[0]].shape[0] / (d.shape[0] - d[col].isnull().sum())])\n",
    "    except:\n",
    "        error_list.append(col)\n",
    "tz_df = pd.DataFrame(tz_list,columns = ['特征名称','同值比例'])\n",
    "del_cols = tz_df['特征名称'][tz_df['同值比例'] >= 0.9]\n",
    "print('删除特征的个数：',len(del_cols))\n",
    "print('出错特征的个数：',len(error_list))\n",
    "print('删除的特征：',del_cols.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4032c90c-2e9f-47ff-9b43-383d7419a1d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "d=d.drop(columns=['GNDVI_kaihua','NDVI_chousui','OSAVI_chousui',\n",
    "                   'SVAI_chousui','SR_chousui','NAME','DVI_yunsui', \n",
    "                  'EVI_yunsui', 'EVI2_yunsui', 'GCVI_yunsui', 'Lai_500m_yunsui', 'MCARI_yunsui', 'MSR_yunsui', 'NDVI_yunsui', \n",
    "                  'OSAVI_yunsui', 'SVAI_yunsui', 'WDVI_yunsui','NAME','soil_temperature_level_2_shouhuo',\n",
    "                  'VDVI_shouhuo','AvgSurfT_inst_chengshu'])\n",
    "dd=dd.drop(columns=['GNDVI_kaihua','NDVI_chousui','OSAVI_chousui',\n",
    "                   'SVAI_chousui','SR_chousui','NAME','DVI_yunsui', \n",
    "                  'EVI_yunsui', 'EVI2_yunsui', 'GCVI_yunsui', 'Lai_500m_yunsui', 'MCARI_yunsui', 'MSR_yunsui', 'NDVI_yunsui', \n",
    "                  'OSAVI_yunsui', 'SVAI_yunsui', 'WDVI_yunsui','NAME','soil_temperature_level_2_shouhuo',\n",
    "                  'VDVI_shouhuo','AvgSurfT_inst_chengshu'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4a117973-8634-4679-8251-f818d42ab8c0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#d=d.drop(columns=['GNDVI_chousui','SR_yunsui','WDRVI_chousui','SVAI_chousui','SR_chousui'])\n",
    "#dd=dd.drop(columns=['GNDVI_chousui','SR_yunsui','WDRVI_chousui','SVAI_chousui','SR_chousui'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "299d2aaa-7024-4510-a82e-7a8c7e2bce87",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "### 全部入模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0fc106ad-358a-451e-bbdf-57729747d20a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train =d.drop(columns = [\"亩产\"])\n",
    "y_train = d['亩产']\n",
    "x_test =dd.drop(columns = [\"亩产\"])\n",
    "y_test = dd['亩产']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef9e4a27-8669-49ea-b699-edba291bb268",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = xgb.XGBRegressor(n_estimators=150,\n",
    "                                 max_depth=5,\n",
    "                                 learning_rate=0.1,\n",
    "                                 \n",
    "                                 random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "44180510-e0eb-449d-bc96-8b985e0ca0c3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "xgboost_model_fit = model.fit(x_train, y_train)\n",
    "y_pred_bo = xgboost_model_fit.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "136d8f93-7a6c-4694-bbb0-cbc45073abd5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>平均绝对误差（MAPE）</th>\n",
       "      <th>平均绝对误差（MAE）</th>\n",
       "      <th>均方根误差（RMSE）</th>\n",
       "      <th>决定系数（R^2）</th>\n",
       "      <th>解释方差</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bo-xgboost</th>\n",
       "      <td>0.090147</td>\n",
       "      <td>530.809455</td>\n",
       "      <td>679.902345</td>\n",
       "      <td>0.743432</td>\n",
       "      <td>0.74375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            平均绝对误差（MAPE）  平均绝对误差（MAE）  均方根误差（RMSE）  决定系数（R^2）     解释方差\n",
       "bo-xgboost      0.090147   530.809455   679.902345   0.743432  0.74375"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 平均绝对百分比误差\n",
    "mape=mean_absolute_percentage_error(y_test,y_pred_bo)\n",
    "# 计算均方根误差（RMSE）\n",
    "rmse = mean_squared_error(y_test, y_pred_bo, squared=False)\n",
    "# 计算平均绝对误差（MAE）\n",
    "mae = mean_absolute_error(y_test, y_pred_bo)\n",
    "# 计算决定系数（R^2）\n",
    "r2 = r2_score(y_test, y_pred_bo)\n",
    "# 计算解释方差分\n",
    "explained_var = explained_variance_score(y_test, y_pred_bo)\n",
    "\n",
    "results1 = pd.DataFrame()\n",
    "results1['平均绝对误差（MAPE）'] = [mape]\n",
    "results1['平均绝对误差（MAE）'] = [mae]\n",
    "results1['均方根误差（RMSE）'] = [rmse]\n",
    "results1['决定系数（R^2）'] = [r2]\n",
    "results1['解释方差'] = [explained_var]\n",
    "results1.index = ['bo-xgboost']\n",
    "results1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "43a37742-b0a1-4ac8-9f09-39d1fe7992bf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_test</th>\n",
       "      <th>y_pred</th>\n",
       "      <th>precent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7467.000138</td>\n",
       "      <td>7284.002441</td>\n",
       "      <td>-2.45%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5972.666001</td>\n",
       "      <td>6906.383301</td>\n",
       "      <td>15.63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5687.791498</td>\n",
       "      <td>4752.098145</td>\n",
       "      <td>-16.45%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7959.597222</td>\n",
       "      <td>7391.888672</td>\n",
       "      <td>-7.13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7662.496889</td>\n",
       "      <td>7128.543457</td>\n",
       "      <td>-6.97%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6613.422500</td>\n",
       "      <td>7576.770996</td>\n",
       "      <td>14.57%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>7579.469809</td>\n",
       "      <td>7140.886230</td>\n",
       "      <td>-5.79%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5754.140236</td>\n",
       "      <td>6510.867676</td>\n",
       "      <td>13.15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>5467.128359</td>\n",
       "      <td>5880.130371</td>\n",
       "      <td>7.55%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>6637.200003</td>\n",
       "      <td>5790.942383</td>\n",
       "      <td>-12.75%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          y_test       y_pred  precent\n",
       "0    7467.000138  7284.002441   -2.45%\n",
       "1    5972.666001  6906.383301   15.63%\n",
       "2    5687.791498  4752.098145  -16.45%\n",
       "3    7959.597222  7391.888672   -7.13%\n",
       "4    7662.496889  7128.543457   -6.97%\n",
       "..           ...          ...      ...\n",
       "97   6613.422500  7576.770996   14.57%\n",
       "98   7579.469809  7140.886230   -5.79%\n",
       "99   5754.140236  6510.867676   13.15%\n",
       "100  5467.128359  5880.130371    7.55%\n",
       "101  6637.200003  5790.942383  -12.75%\n",
       "\n",
       "[102 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_array = np.array(y_test)\n",
    "\n",
    "y_pred_series = pd.Series(y_pred_bo, name='y_pred')\n",
    "y_test_series = pd.Series(y_test_array, name='y_test')\n",
    "dftest= pd.concat([y_test_series, y_pred_series], axis=1)\n",
    "dftest['precent'] = ((dftest['y_pred'] - dftest['y_test']) / dftest['y_test']) * 100\n",
    "dftest['precent'] = dftest['precent'].apply(lambda x: '{:.2f}%'.format(x))\n",
    "dftest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6b1994e2-af3b-478a-992d-e4128e188f91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test_sorted = np.sort(y_test)\n",
    "y_pred_sorted = y_pred_bo[np.argsort(y_test)]\n",
    "\n",
    "# 绘制折线对比图\n",
    "plt.plot(range(len(y_test_sorted)), y_test_sorted, label='Actual')\n",
    "plt.plot(range(len(y_pred_sorted)), y_pred_sorted, label='Predicted',linestyle='--')\n",
    "#plt.xlabel('Index')\n",
    "plt.ylabel('Value',fontsize=16)\n",
    "plt.title('Actual vs. Predicted (Bo-XGBoost)',fontsize=16)\n",
    "plt.legend()\n",
    "plt.ylim(1000, 10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "df4a379a-10bb-4f49-8b5f-1490cbda5d25",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(y_test, y_pred_bo, alpha=0.5, label='Data Points')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.title('bh-XGBoost Scatter Plot of Actual vs. Predicted Values')\n",
    "\n",
    "# 添加对角线\n",
    "max_value = max(max(y_test), max(y_pred_bo))\n",
    "min_value = min(min(y_test), min(y_pred_bo))\n",
    "plt.plot([min_value, max_value], [min_value, max_value], color='red', linestyle='--', linewidth=2, label='Perfect Prediction')\n",
    "\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3896133d-f939-4563-8d25-5aed0e0b5413",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
